M. Abaza, F. Anctil, V. Fortin, and L. Perreault, On the incidence of meteorological and hydrological processors: Effect of resolution, sharpness and reliability of hydrological ensemble forecasts, J. Hydrol, vol.555, pp.371-384, 2017.

P. Abbaszadeh, H. Moradkhani, Y. , and H. , Enhancing hydrologic data assimilation by evolutionary Particle Filter and Markov Chain Monte Carlo, Adv, Water Resour, vol.111, pp.192-204, 2018.

P. Allamano, F. Laio, and P. Claps, Effects of disregarding seasonality on the distribution of hydrological extremes, Hydrol. Earth Syst. Sci, vol.15, pp.3207-3215, 2011.

F. Anctil, C. Perrin, A. , and V. , Impact of the length of observed records on the performance of ANN and of conceptual parsimonious rainfall-runoff forecasting models, Environ. Model. Softw, vol.19, pp.357-368, 2004.
URL : https://hal.archives-ouvertes.fr/hal-02582806

V. Andréassian, A. Hall, N. Chahinian, and J. Schaake, Introduction and Synthesis: Why should hydrologists work on a large number of basin data sets?, International Association of Hydrological Sciences, issue.307, pp.1-5, 2006.

V. Andréassian, C. Perrin, L. Berthet, N. Le-moine, J. Lerat et al., Crash tests for a standardized evaluation of hydrological models, Hydrol. Earth Syst. Sci, vol.13, pp.1757-1764, 2009.

V. Andréassian, N. Le-moine, C. Perrin, M. Ramos, L. Oudin et al., All that glitters is not gold: the case of calibrating hydrological models, Hydrol. Process, vol.26, pp.2206-2210, 2012.

S. Barbetta, G. Coccia, T. Moramarco, L. Brocca, and E. Todini, The multi temporal/multi-model approach to predictive uncertainty assessment in real-time flood forecasting, J. Hydrol, vol.551, pp.555-576, 2017.

J. C. Bennett, D. E. Robertson, D. L. Shrestha, Q. Wang, D. Enever et al., A System for Continuous Hydrological Ensemble Forecasting (SCHEF) to lead times of 9 days, J. Hydrol, vol.519, pp.2832-2846, 2014.

L. Berthet, Flood forecasting at the hourly time-step: for a better assimilation of flow information in hydrological modelling, Doctoral School GRN, 2010.

L. Berthet and O. Piotte, International survey for good practices in forecasting uncertainty assessment and communication, EGU General Assembly, vol.16, pp.2014-8579, 2014.

L. Berthet, V. Andréassian, C. Perrin, J. , and P. , How crucial is it to account for the antecedent moisture conditions in flood forecasting? Comparison of event-based and continuous approaches on 178 catchments, Hydrol. Earth Syst. Sci, vol.13, pp.819-831, 2009.
URL : https://hal.archives-ouvertes.fr/hal-00583212

L. Berthet, V. Andréassian, C. Perrin, and C. Loumagne, How significant are quadratic criteria? Part 2. On the relative contribution of large flood events to the value of a quadratic criterion, Hydrolog. Sci. J, vol.55, pp.1063-1073, 2010.
URL : https://hal.archives-ouvertes.fr/hal-02594522

A. R. Bock, W. H. Farmer, L. E. Hay, K. Bogner, F. Pappenberger et al., Quantifying uncertainty in simulated streamflow and runoff from a continental-scale monthly water balance model, Hydrol. Earth Syst. Sci, vol.122, pp.1085-1094, 2012.

F. Bourgin, How to quantify predictive uncertainty in hydrological modelling? Exploratory work on a large sample of catchments, Doctoral School GRNE, 2014.

F. Bourgin, M. Ramos, G. Thirel, V. Andréassian, F. Bourgin et al., Investigating the interactions between data assimilation and postprocessing in hydrological ensemble forecasting, Hydrol. Earth Syst. Sci, vol.519, pp.2535-2546, 2014.
URL : https://hal.archives-ouvertes.fr/hal-02601490

G. E. Box and D. R. Cox, An Analysis of Transformations, J. Roy. Stat. Soc. B, vol.26, pp.211-252, 1964.

L. Breiman, Statistical modeling: The two cultures, Stat. Sci, vol.16, pp.199-215, 2001.

J. B. Bremnes, Constrained Quantile Regression Splines for Ensemble Postprocessing, Mon. Weather Rev, vol.147, pp.1769-1780, 2019.

P. Brigode, L. Oudin, and C. Perrin, Hydrological model parameter instability: A source of additional uncertainty in estimating the hydrological impacts of climate change?, J. Hydrol, vol.476, pp.410-425, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00785252

P. Brigode, B. Génot, F. Lobligeois, and O. Delaigue, Summary sheets of watershed-scale hydroclimatic observed data for France, Portail Data INRAE, p.2020

D. Buzzati, Il deserto dei Tartari (The Tartar Steppe), Rizzoli, Milano, 1940.

H. Cigizoglu, Estimation, forecasting and extrapolation of river flows by artificial neural networks, Hydrolog. Sci. J, vol.48, pp.349-362, 2003.

G. Coccia and E. Todini, Recent developments in predictive uncertainty assessment based on the model conditional processor approach, Hydrol. Earth Syst. Sci, vol.15, pp.3253-3274, 2011.

L. Coron, V. Andreassian, C. Perrin, J. Lerat, J. Vaze et al., Crash testing hydrological models in contrasted climate conditions: An experiment on 216 Australian catchments, Water Resour. Res, vol.48, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00763573

D. Del-giudice, M. Honti, A. Scheidegger, C. Albert, P. Reichert et al., Improving uncertainty estimation in urban hydrological modeling by statistically describing bias, Hydrol. Earth Syst. Sci, vol.17, pp.4209-4225, 2013.

J. Demargne, L. Wu, S. K. Regonda, J. D. Brown, H. Lee et al., The Science of NOAA's Operational Hydrologic Ensemble Forecast Service, B. Am. Meteorol. Soc, vol.95, pp.79-98, 2014.

D. Demeritt, H. Cloke, F. Pappenberger, J. Thielen, J. Bartholmes et al., Ensemble predictions and perceptions of risk, uncertainty, and error in flood forecasting, Environ. Hazards, vol.7, pp.115-127, 2007.

N. Dogulu, P. López-lópez, D. P. Solomatine, A. H. Weerts, and D. L. Shrestha, Estimation of predictive hydrologic uncertainty using the quantile regression and UNEEC methods and their comparison on contrasting catchments, Hydrol. Earth Syst. Sci, vol.19, pp.3181-3201, 2015.

A. Ficchì, C. Perrin, A. , and V. , Impact of temporal resolution of inputs on hydrological model performance: An analysis based on 2400 flood events, J. Hydrol, vol.538, pp.454-470, 2016.

C. Furusho, C. Perrin, J. Viatgé, L. , A. et al., Collaborative work between operational forecasters and scientists for better flood forecasts, 2016.
URL : https://hal.archives-ouvertes.fr/hal-02604707

E. Gaume, V. Bain, P. Bernardara, O. Newinger, M. Barbuc et al., A compilation of data on European flash floods, J. Hydrol, vol.367, pp.70-78, 2009.
URL : https://hal.archives-ouvertes.fr/hal-00400570

O. Giustolisi and D. Laucelli, Improving generalization of artificial neural networks in rainfall-runoff modelling, Hydrolog. Sci. J, vol.50, pp.439-457, 2005.

T. Gneiting, F. Balabdaoui, and A. E. Raftery, Probabilistic forecasts, calibration and sharpness, J. Roy. Stat. Soc. B, vol.69, pp.243-268, 2007.
URL : https://hal.archives-ouvertes.fr/hal-00363242

H. V. Gupta, C. Perrin, G. Blöschl, A. Montanari, R. Kumar et al., Large-sample hydrology: a need to balance depth with breadth, Hydrol. Earth Syst. Sci, vol.18, pp.463-477, 2014.
URL : https://hal.archives-ouvertes.fr/hal-00949351

S. Hemri, D. Lisniak, B. Klein, and H. Hersbach, Decomposition of the continuous ranked probability score for ensemble prediction systems, Water Resour. Res, vol.51, pp.559-570, 2000.

C. Imrie, S. Durucan, and A. Korre, River flow prediction using artificial neural networks: Generalisation beyond the calibration range, J. Hydrol, vol.233, pp.138-153, 2000.

K. S. Kelly and R. Krzysztofowicz, A bivariate meta-Gaussian density for use in hydrology, Stoch. Hydrol. Hydraul, vol.11, pp.17-31, 1997.

V. Klemes, Operational testing of hydrological simulation models, Hydrolog. Sci. J. -Journal Des Sciences Hydrologiques, vol.31, pp.13-24, 1986.

R. Krzysztofowicz, Bayesian theory of probabilistic forecasting via deterministic hydrologic model, Water Resour. Res, vol.35, pp.2739-2750, 1999.

R. Krzysztofowicz and C. J. Maranzano, Hydrologic uncertainty processor for probabilistic stage transition forecasting, J. Hydro, vol.293, pp.57-73, 2004.

F. Laio and S. Tamea, Verification tools for probabilistic forecasts of continuous hydrological variables, Hydrol. Earth Syst. Sci, vol.11, pp.1267-1277, 1267.
URL : https://hal.archives-ouvertes.fr/hal-00305072

M. Lang, K. Pobanz, B. Renard, E. Renouf, and E. Sauquet, Extrapolation of rating curves by hydraulic modelling, with application to flood frequency analysis, Hydrolog. Sci. J, vol.55, pp.883-898, 2010.
URL : https://hal.archives-ouvertes.fr/hal-02593439

D. Legates and G. Mccabe, Evaluating the use of 'goodness-offit' measures in hydrologic and hydroclimatic model validation, Water Resour. Res, vol.35, pp.233-241, 1999.

I. Leleu, I. Tonnelier, R. Puechberty, P. Gouin, I. Viquendi et al., Re-founding the national information system designed to manage and give access to hydrometric data, 2014.

M. Li, Q. J. Wang, and J. Bennett, Accounting for seasonal dependence in hydrological model errors and prediction uncertainty, Water Resour. Res, vol.49, pp.5913-5929, 2013.

W. Li, Q. Duan, C. Miao, A. Ye, W. Gong et al., A review on statistical postprocessing methods for hydrometeorological ensemble forecasting, Wiley Interdisciplin. Rev.: Water, vol.4, 1246.

K. Liano, Robust error measure for supervised neural network learning with outliers, IEEE T. Neural Netw, vol.7, pp.246-250, 1996.

F. Lobligeois, V. Andréassian, C. Perrin, P. Tabary, and C. Loumagne, When does higher spatial resolution rainfall information improve streamflow simulation? An evaluation using 3620 flood events, Hydrol. Earth Syst. Sci, vol.18, pp.575-594, 2014.
URL : https://hal.archives-ouvertes.fr/hal-00952657

D. Mcinerney, M. Thyer, D. Kavetski, J. Lerat, and G. Kuczera, Improving probabilistic prediction of daily streamflow by identifying Pareto optimal approaches for modeling heteroscedastic residual errors, Water Resour. Res, vol.53, pp.2199-2239, 2017.

R. Merz, J. Parajka, and G. Blöschl, Time stability of catchment model parameters: Implications for climate impact analyses, Water Resour. Res, vol.47, p.2531, 2011.

C. Michel, Que peut-on faire en hydrologie avec un modèle conceptuel à un seul paramètre?, 1983.

A. Montanari, Uncertainty of Hydrological Predictions, Treatise on Water Science, pp.459-478, 2011.

A. Montanari and G. Grossi, Estimating the uncertainty of hydrological forecasts: A statistical approach, Water Resour. Res, vol.44, 2008.

H. Moradkhani, K. L. Hsu, H. Gupta, and S. Sorooshian, Uncertainty assessment of hydrologic model states and parameters: Sequential data assimilation using the particle filter, Water Resour. Res, vol.41, 2005.

H. Moradkhani, S. Sorooshian, H. V. Gupta, and P. R. Houser, Dual state-parameter estimation of hydrological models using ensemble Kalman filter, Adv. Water Resour, vol.28, pp.135-147, 2005.

M. Morawietz, C. Xu, L. Gottschalk, and L. M. Tallaksen, Systematic evaluation of autoregressive error models as postprocessors for a probabilistic streamflow forecast system, J. Hydrol, vol.407, pp.58-72, 2011.

L. Oudin, F. Hervieu, C. Michel, C. Perrin, V. Andreassian et al., Which potential evapotranspiration input for a lumped rainfall-runoff model? Part 2 -Towards a simple and efficient potential evapotranspiration model for rainfall-runoff modelling, J. Hydrol, vol.303, pp.290-306, 2005.
URL : https://hal.archives-ouvertes.fr/hal-02584170

T. C. Pagano, D. L. Shrestha, Q. J. Wang, D. Robertson, and P. Hapuarachchi, Ensemble dressing for hydrological applications, Hydrol. Process, vol.27, pp.106-116, 2013.

T. C. Pagano, A. W. Wood, M. Ramos, H. L. Cloke, F. Pappenberger et al., Challenges of Operational River Forecasting, J. Hydrometeorol, vol.15, pp.1692-1707, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01132102

F. Pappenberger and K. J. Beven, Ignorance is bliss: Or seven reasons not to use uncertainty analysis, Water Resour. Res, vol.42, 2006.

F. Pappenberger, J. Thielen, D. Medico, and M. , The impact of weather forecast improvements on large scale hydrology: analysing a decade of forecasts of the European Flood Alert System, Hydrol. Process, vol.25, pp.1091-1113, 2011.

F. Pappenberger, T. C. Pagano, J. D. Brown, L. Alfieri, D. A. Lavers et al., Hydrological Ensemble Prediction Systems Around the Globe, 2016.

C. Perrin, C. Michel, V. Andreassian, C. Perrin, L. Oudin et al., Impact of limited streamflow data on the efficiency and the parameters of rainfall-runoff models, Hydrolog. Sci. J, vol.279, pp.131-151, 2003.

M. Ramos, J. Bartholmes, J. T. Pozo, and .. , Development of decision support products based on ensemble forecasts in the European flood alert system, Atmos. Sci. Lette, vol.8, pp.113-119, 2007.
URL : https://hal.archives-ouvertes.fr/hal-02589839

P. Reichert and J. Mieleitner, Analyzing input and structural uncertainty of nonlinear dynamic models with stochastic, time-dependent parameters, Water Resour. Res, vol.45, p.10402, 2009.

B. Renard, D. Kavetski, G. Kuczera, M. Thyer, and S. W. Franks, Understanding predictive uncertainty in hydrologic modeling: The challenge of identifying input and structural errors, Water Resour. Res, vol.46, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00456159

P. Salamon and L. Feyen, Assessing parameter, precipitation, and predictive uncertainty in a distributed hydrological model using sequential data assimilation with the particle filter, J. Hydrol, vol.376, pp.428-442, 2009.

G. Schoups and J. A. Vrugt, A formal likelihood function for parameter and predictive inference of hydrologic models with

L. Berthet, Design and experimentation of an empirical multistructure framework for accurate, sharp and reliable hydrological ensembles, Water Resour. Res, vol.46, pp.313-340, 2010.

S. Sharma, R. Siddique, S. Reed, P. Ahnert, P. Mendoza et al., Relative effects of statistical preprocessing and postprocessing on a regional hydrological ensemble prediction system, Hydrol. Earth Syst. Sci, vol.22, pp.1831-1849, 2018.

S. K. Singh, H. Mcmillan, and A. Bardossy, Use of the data depth function to differentiate between case of interpolation and extrapolation in hydrological model prediction, J. Hydrol, vol.477, pp.213-228, 2013.

D. P. Solomatine and D. L. Shrestha, A novel method to estimate model uncertainty using machine learning techniques, Water Resour, Res, vol.45, 2009.

P. Tabary, P. Dupuy, G. L'henaff, C. Gueguen, L. Moulin et al., A 10-year (1997-2006) reanalysis of Quantitative Precipitation Estimation over France: methodology and first results, vol.351, pp.255-260, 2012.

J. Thielen, J. Bartholmes, M. Ramos, and A. De-roo, The European Flood Alert System -Part 1: Concept and development, Hydrol. Earth Syst. Sci, vol.13, pp.125-140, 2009.
URL : https://hal.archives-ouvertes.fr/hal-00330814

E. Todini, Role and treatment of uncertainty in real-time flood forecasting, Hydrol. Process, vol.18, pp.2743-2746, 2004.

E. Todini, Hydrological catchment modelling: past, present and future, Hydrol. Earth Syst. Sci, vol.11, pp.468-482, 2007.
URL : https://hal.archives-ouvertes.fr/hal-00305632

E. Todini, A model conditional processor to assess predictive uncertainty in flood forecasting, Int. J. River Basin Manage, vol.6, pp.123-137, 2008.

E. Todini, P. C. Baveye, M. Laba, J. Mysiak, A. Valéry et al., As simple as possible but not simpler': What is useful in a temperature-based snow-accounting routine? Part 2 -Sensitivity analysis of the Cemaneige snow accounting routine on 380 catchments, Uncertainties in Environmental Modelling and Consequences for Policy Making, vol.517, pp.1176-1187, 2009.

N. Van-steenbergen, J. Ronsyn, and P. Willems, A non-parametric data-based approach for probabilistic flood forecasting in support of uncertainty communication, Environ. Model. Softw, vol.33, pp.92-105, 2012.

J. Vaze, D. Post, F. Chiew, J. Perraud, N. Viney et al., Climate non-stationarity -Validity of calibrated rainfall-runoff models for use in climate change studies, J. Hydrol, vol.394, pp.447-457, 2010.

J. A. Velazquez, F. Anctil, and C. Perrin, Performance and reliability of multimodel hydrological ensemble simulations based on seventeen lumped models and a thousand catchments, Hydrol. Earth Syst. Sci, vol.14, pp.2303-2317, 2010.
URL : https://hal.archives-ouvertes.fr/hal-02594613

J. S. Verkade and M. G. Werner, Estimating the benefits of single value and probability forecasting for flood warning, Hydrol. Earth Syst. Sci, vol.15, pp.3751-3765, 2011.

J. S. Verkade, J. Brown, F. Davids, P. Reggiani, and A. Weerts, Estimating predictive hydrological uncertainty by dressing deterministic and ensemble forecasts; a comparison, with application to Meuse and Rhine, J. Hydrol, vol.555, pp.257-277, 2017.

J. Viatgé, T. Pinna, C. Perrin, D. Dorchies, and L. Garandeau, De la prévision des crues à la gestion de crise" (From flood forecasting to crisis management, Proceedings of the SHF conference, p.12, 2018.

J. Viatgé, L. Berthet, R. Marty, F. Bourgin, O. Piotte et al., Towards the real-time production of predictive intervals around streamflow forecasts in Vigicrues in France, 2019.

Q. J. Wang, D. E. Robertson, and F. H. Chiew, A Bayesian joint probability modeling approach for seasonal forecasting of streamflows at multiple sites, Water Resour. Res, vol.45, 2009.

Q. J. Wang, D. L. Shrestha, D. E. Robertson, and P. Pokhrel, A log-sinh transformation for data normalization and variance stabilization, Water Resour. Res, vol.48, 2012.

O. Wani, J. V. Beckers, A. H. Weerts, and D. P. Solomatine, Residual uncertainty estimation using instance-based learning with applications to hydrologic forecasting, Hydrol. Earth Syst. Sci, vol.21, pp.4021-4036, 2017.

A. H. Weerts, H. C. Winsemius, and J. S. Verkade, Estimation of predictive hydrological uncertainty using quantile regression: examples from the National Flood Forecasting System, Hydrol. Earth Syst. Sci, vol.15, pp.255-265, 2011.

R. L. Wilby, Uncertainty in water resource model parameters used for climate change impact assessment, Hydrol. Process, vol.19, pp.3201-3219, 2005.

F. Woldemeskel, D. Mcinerney, J. Lerat, M. Thyer, D. Kavetski et al., Evaluating post-processing approaches for monthly and seasonal streamflow forecasts, Hydrol. Earth Syst. Sci, vol.22, pp.6257-6278, 2018.

D. P. Wright, M. Thyer, and S. Westra, Influential point detection diagnostics in the context of hydrological model calibration, J. Hydrol, vol.527, pp.1161-1172, 2015.

J. Yang, P. Reichert, and K. C. Abbaspour, Bayesian uncertainty analysis in distributed hydrologic modeling: a case study in the Thur River basin (Switzerland), Water Resour. Res, vol.43, p.10401, 2007.

L. Berthet, Uncertainty assessment when forecasting high flows 2041

J. Yang, P. Reichert, K. C. Abbaspour, Y. , and H. , Hydrological modelling of the Chaohe Basin in China: Statistical model formulation and Bayesian inference, J. Hydrol, vol.340, pp.167-182, 2007.

I. Zalachori, M. H. Ramos, R. Garçon, T. Mathevet, and J. Gailhard, Statistical processing of forecasts for hydrological ensemble prediction: a comparative study of different bias correction strategies, Adv. Sci. Res, vol.8, pp.135-141, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00763615